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Comparing Coefficients Across Subpopulations in Gaussian Mixture Regression Models

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  • Shin-Fu Tsai

    (National Taiwan University)

Abstract

When fitting a Gaussian mixture regression model to observed data, estimating a between-group contrast can be a practical issue. One can use the estimate to compare the effects of a particular covariate or a set of covariates across different subpopulations. By applying fiducial generalized pivotal quantities, a small-sample solution is proposed in this paper to obtain interval estimates of between-group contrasts. Specifically, a Markov chain Monte Carlo sampler, which takes the membership uncertainty of each individual into account, is designed to generate realizations from the target distributions for computing the required interval estimates. A plant virus transmission study is first introduced as a motivating example for the present study. Next, the observed data are analyzed to illustrate the proposed method. Based on the simulation results, it is further shown that the proposed method can maintain the empirical coverage rates sufficiently close to the nominal level. Supplementary materials accompanying this paper appear on-line.

Suggested Citation

  • Shin-Fu Tsai, 2019. "Comparing Coefficients Across Subpopulations in Gaussian Mixture Regression Models," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 24(4), pages 610-633, December.
    • Handle: RePEc:spr:jagbes:v:24:y:2019:i:4:d:10.1007_s13253-019-00364-4
    DOI: 10.1007/s13253-019-00364-4
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    References listed on IDEAS

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    Cited by:

    1. Shin‐Fu Tsai, 2020. "Approximate two‐sided tolerance intervals for normal mixture distributions," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(3), pages 367-382, September.

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